A deeper generative adversarial network for grooved cement concrete pavement crack detection. (March 2023) - Zhong, Jingtao; Huyan, Ju; Zhang, Weiguang; Cheng, Hanglin; Zhang, Jing; Tong, Zheng; Jiang, Xi; Huang, Baoshan
Abstract: Periodic grooved cement concrete pavement crack detection is of great importance for pavement condition monitoring and maintenance. The current state-of-the-art (SOTA) detection solutions highly depend on datasets. However, due to the limited access to crack images, more efficient methods are urgently needed to advance the detection of cracking on grooved cement concrete pavement. This study proposes an improved deeper Wasserstein generative adversarial network with gradient penalty (WGAN-GP) to generate datasets of pavement images with a size of 512 × 512 pixels 2 . Poisson bleeding is adopted to create the synthesized grooved cement concrete pavement crack images based on the generated crack images and groove images. The robustness of the proposed improved deeper WGAN-GP model is validated by Faster R-CNN, YOLOv3, and YOLOv4 models trained on original crack images and generated crack images for region-level detection. U-Net and W-segnet are used to achieve pixel-level crack detection to evaluate the effectiveness of proposed model. Results show that the improved deeper WGAN-GP could generate more realistic transverse, longitudinal and oblique crack images. In addition, the Poisson bleeding algorithm contributes to synthesizing grooved cement concrete pavement crack images. Moreover, it is observed that YOLOv3 trained by the augmented dataset could achieve a mean average precision (MAP) of 81.98%, 6% MAP higher than the non-augmented dataset. U-Net and W-segnet benefit from augmented dataset with a better pixel-level segmentation result. Based on the results, it can be concluded that the improved deeper WAGN-GP image generation method can provide a straightforward way to fill the data shortage gap of grooved cement concrete pavement cracks, thus increasing the problem-solving capability of the SOTA crack detection models.